U.S. patent application number 15/702858 was filed with the patent office on 2019-03-14 for automatic harbor surveillance system.
This patent application is currently assigned to United States of America, as Represented by the Secretary of the Navy. The applicant listed for this patent is Greg Anderson, Iryna Dzieciuch, Michael Putnam. Invention is credited to Greg Anderson, Iryna Dzieciuch, Michael Putnam.
Application Number | 20190079213 15/702858 |
Document ID | / |
Family ID | 65630985 |
Filed Date | 2019-03-14 |
United States Patent
Application |
20190079213 |
Kind Code |
A1 |
Dzieciuch; Iryna ; et
al. |
March 14, 2019 |
Automatic Harbor Surveillance System
Abstract
Systems and methods for harbor surveillance according to several
embodiments of the present invention can include a plurality of
buoys that are arranged in a harbor in a predetermined pattern. The
buoys can have various components for computing buoy position data,
including but not limited to an accelerometer, gyroscope, GPS,
compass, and a transmitter. The buoys can be equidistant from each
other, and in some embodiments, the buoys can be submerged. The
buoy position data can be received by a remote receiver. A
processor that is connected to the received can convert the buoy
position data, or change in data due to movement of a buoy cause by
a Kelvin wake, which is further due to vessel movement through the
harbor, into a determination of vessel presence, course and speed,
using a fuzzy neural network algorithm.
Inventors: |
Dzieciuch; Iryna; (San
Diego, CA) ; Putnam; Michael; (San Diego, CA)
; Anderson; Greg; (San Diego, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Dzieciuch; Iryna
Putnam; Michael
Anderson; Greg |
San Diego
San Diego
San Diego |
CA
CA
CA |
US
US
US |
|
|
Assignee: |
United States of America, as
Represented by the Secretary of the Navy
Arlington
VA
|
Family ID: |
65630985 |
Appl. No.: |
15/702858 |
Filed: |
September 13, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
B63B 2022/006 20130101;
B63B 22/00 20130101; G06N 3/0481 20130101; G06N 3/08 20130101; G01V
9/00 20130101; G06N 3/0436 20130101; G01V 1/3843 20130101; H04L
67/12 20130101; G06N 3/084 20130101 |
International
Class: |
G01V 9/00 20060101
G01V009/00; G06N 3/04 20060101 G06N003/04; G06N 3/08 20060101
G06N003/08; B63B 22/00 20060101 B63B022/00 |
Goverment Interests
FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT
[0001] The United States Government has ownership rights in this
invention. Licensing inquiries may be directed to Office of
Research and Technical Applications, Space and Naval Warfare
Systems Center, Pacific, Code 72120, San Diego, Calif., 92152;
telephone (619) 553-5118; email: ssc_pac_t2@navy.mil, referencing
103747.
Claims
1. A surveillance system, comprising: a plurality of buoys; each
said buoy having means for determining buoy position data transfer
data and a transmitter for transmitting said position data; a means
for receiving said buoy position data; and, a processor, said
processor having non-transitory written instruction for converting
said buoy position data into an indication of the presence of a
vessel.
2. The system of claim 1, wherein said indication is positive, and
buoy position data is generated by motion of said buoys, and said
motion of said buoys is caused by a Kelvin wake generated by said
vessel.
3. The system of claim 2, wherein said buoys are equidistant from
each other.
4. The system of claim 2, wherein said buoys are not equidistant
from each other, but further wherein said each buoy has a known
distance "d.sub.i" and said written instructions incorporate said
known distance d.sub.i in generating said algorithm.
5. The system of claim 1, wherein said buoys are submerged.
6. The system of claim 1, wherein said algorithm is a fuzzy neural
network algorithm.
7. A method for conducting harbor surveillance comprising the steps
of: A) placing a plurality of buoys in said harbor in a
predetermined pattern; B) determining buoy position data transfer
data for each said buoy from said step A) C) transmitting said
position data from said step B) a receiver; and, D) converting said
buoy position data into an indication of the presence of a vessel
in said harbor.
8. The method of claim 7, wherein said indication from said step D)
is positive, and buoy position data from said step B) is generated
by motion of said buoys, and said motion of said buoys is generated
by a Kelvin wake generated by said vessel.
9. The method of claim 7 wherein said predetermined pattern from
said step A) is a line with said buoys are being equidistant from
each other.
10. The method of claim 7, wherein said predetermined pattern from
said step A) is a line with said buoys not being equidistant from
each other, but further wherein said each buoy has a known distance
"d.sub.i" and said step D) is accomplished using written
instructions that incorporate said known distance "d.sub.i".
11. The method of claim 7, wherein said steps A) is accomplished
with said buoys that are submerged.
12. The method of claim 7, wherein said step D) is accomplished
using a fuzzy neural network algorithm.
Description
FIELD OF THE INVENTION
[0002] The present invention pertains generally to harbor
surveillance systems. More specifically, the present invention can
pertain to systems and method for detecting objects moving on the
water's surface using an array of sensing buoys. The present
invention can be particularly, but not exclusively, useful as a
harbor surveillance system that uses sensing buoys, which are
equipped with a motion sensing detector and a data communication
link. The buoys are able to record and send motion data readings
from multiple sensors to a server. These readings can be analyzed
in real-time by an artificial neural network algorithm, to discern
Kelvin wake patterns created by moving objects.
BACKGROUND OF THE INVENTION
[0003] Often times it is desirable to conduct harbor surveillance
operations, for safety and security reasons. To do this, it is
often desirable to be able to detect objects that are transiting
through the harbor. One way to do this is by using radar. But
radars can require an external source of power. Alternatively,
cameras can be used to detect and record vessel harbor transits.
But visual camera methods may not be particularly effective in fog
and low visibility situations. And whatever method is used, it can
be desirable to minimize the personnel required to operate and
maintain such a harbor surveillance system.
[0004] Another method for harbor surveillance could be to take
advantage of man-made Kelvin wakes on the surface of the water,
which can be caused by vessels moving through a fluid (water).
Kelvin wakes have been studied since the 1950's, however, there is
no applied approach that describes how to unitize this knowledge
for detecting moving objects of different types. Also, there is no
exact scientific method that describes how to measure wake
displacement, and convert that wake displacement into a reliable
indication of a vessel's presence, even though physics of this
phenomena has been described.
[0005] In view of the above, it can be an object of the present
invention to provide systems and methods for harbor surveillance
that can automatically monitor harbor traffic, without human
operator intervention. Another object of the present invention can
be to provide systems and methods for automatic harbor surveillance
that can use Kelvin wake phenomena caused by vessels moving through
the harbor, to determine the presence, course and speed of the
vessel. Another object of the present invention can be to provide
systems and methods for automatic harbor surveillance that can use
the Kelvin wake phenomena to detect vessels in low visibility
conditions, even if the vessels cannot be seen. Yet another object
of the present invention can be to provide systems and methods for
harbor surveillance that can use a low power, renewable energy
source, and that can yield real-time notifications on vessel
movement through the harbor. Still another object of the present
invention can be to provide systems and methods for harbor
surveillance that can be easily implemented in a cost-effective
manner.
SUMMARY OF THE INVENTION
[0006] Systems and methods for harbor surveillance according to
several embodiments of the present invention can include a
plurality of buoys that are arranged in a harbor in a predetermined
pattern. The buoys can have various sub-components which can
include an accelerometer, gyroscope, GPS, compass, and a
transmitter, for computing buoy position data. The buoys can be
equidistant from each other at a distance "d", although in some
cases (due to the geography of the harbor, for example), the buoys
can have a different distance "d.sub.i". In some embodiments, the
buoys can be submerged.
[0007] A receiver can receive the transmitted buoy position data
for each buoy, and a processor can be connected to the receiver for
manipulating the buoy position data set. The processor, using a
fuzzy neural network algorithm set of non-transitory written
instructions, can use the buoy position data, which can be caused
by Kelvin wake(s) that are generated by objects in motion through
the water, to determine if an object is present (or not) in the
harbor. If an object is present, the systems and methods can
determine the course and speed of the object.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] The novel features of the present invention will be best
understood from the accompanying drawings, taken in conjunction
with the accompanying description, in which similarly-referenced
characters refer to similarly-referenced parts, and in which:
[0009] FIG. 1 is a prior art, black-and-white aerial photograph,
which illustrates Kelvin wake phenomena generated by an object
passing through a fluid (vessel through water);
[0010] FIG. 2 is a prior art schematic of the Kelvin wake of FIG.
1;
[0011] FIG. 3 is a block diagram of the automatic harbor
surveillance system of the present invention according to several
embodiments;
[0012] FIG. 4 is a graph of translational buoy data versus time for
a representational buoy from the system of FIG. 3;
[0013] FIG. 5 is a graph of rotational buoy data versus time for a
representational buoy from the system of FIG. 3;
[0014] FIG. 6 is a block diagram that depicts input temporal
displacement data and output data object characteristics data from
the system of FIG. 3;
[0015] FIG. 7 is a block diagram that depicts input temporal
displacement data and output data vessel type data from the system
of FIG. 3;
[0016] FIG. 8A is a side elevational view of a top portion of a
representative buoy from the system of FIG. 3;
[0017] FIG. 8B is a block diagram of the lower portion of the buoy
of FIG. 4A, which can illustrate the component parts of the buoys
for the system of FIG. 3 in greater detail; and,
[0018] FIG. 9 is a block diagram that is illustrative of steps that
can be taken to accomplish some of the methods of the present
invention according to several embodiments.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0019] Referring initially to FIGS. 1-2 for a brief overview, from
a fluid dynamics perspective, a wake can be thought of a wave
pattern on the water surface that is downstream of an object in a
fluid flow. The wake can be produced by a moving object (e.g. a
ship), and can be caused by pressure differences of the fluids
above and below the free surface and gravity (or surface tension),
or both. For purposes of this disclosure, and referring to FIG. 1,
waterfowl and boats or other objects moving across the surface of
water can produce a wake pattern, first explained mathematically by
Lord Kelvin and generally known in the prior art as a Kelvin wake
pattern.
[0020] As shown in FIG. 2, a Kelvin wake pattern can consist of two
wake lines that form the arms of a chevron, or "V", with the source
of the wake (vessel 15 in FIG. 2) at the vertex of the V. For
sufficiently slow motion, each wake line can be offset from the
path of the wake source by around arc sin (1/3)=19.47.degree. and
can be made up of feathery wavelets angled at roughly 53.degree. to
the path. The inside of the V in FIG. 2 (of total opening
39.degree. as indicated above) can be filled with transverse curved
waves, each of which is an arc of a circle centered at a point
lying on the path at a distance twice that of the arc to the wake
source. This pattern can be independent of the speed and size of
the wake source over a significant range of values.
[0021] However, the pattern changes at high speeds (only), for
example, above a hull Froude number of approximately 0.5. Then, as
the source's speed increases, the transverse waves diminish and the
points of maximum amplitude on the wavelets form a second V within
the wake pattern, which grows narrower with the increased speed of
the source. Parts of the pattern may be obscured by the effects of
propeller wash, and tail eddies behind the stern or the boat, and
by the boat being a large object and not a point source. Also, the
water need not be stationary, but may be moving as in a large
river. In such cases, the important consideration then can be the
velocity of the water relative to a boat or other object causing a
wake. But for a harbor/restricted waters scenario, such as a river
passage, the Kelvin wake geometry remains sufficiently constant to
allow the system and methods of the present invention to take
advantage of the geometry.
[0022] From the above, it can be seen that Kelvin wake phenomena
can be used as a reliable predictor of vessel motions in a harbor
over a wide range of objects and speeds. To take advantage of these
phenomena, and referring now to FIG. 3, the harbor surveillance
system of the present invention can be shown and can be generally
designated by reference character 10. As shown system 10 can
include a plurality of "smart" buoys 12a-12n. As used herein, the
term "smart" can mean that buoy 12 can have means for transmitting
and receiving data from a network, including but not limited to a
radio network, and intranet, an internet server that can be
connected to the internet, and so on. The buoys 12 can be spaced
apart by a predetermined distance "d". The buoys can be equidistant
from each by the same distance "d", or in other embodiments, it can
be advantageous to space the buoys by different distances
"d.sub.i", for example, if the harbor topography or geography make
it advantageous to do so. As long as each distance d.sub.i is
known, the systems and methods for harbor detection can be
effective. The buoys 12 can be moored to the harbor bottom at a
predesignated location with an anchor, piling or similar type of
structure (not shown in the Figures). Buoys 12 can placed in a
straight line with a set distance d of 1 to 50 meters, depending on
type, size, speed, and direction of the expected moving object, as
well as the size and typical weather conditions of the harbor to be
surveyed, and the anticipated amount of harbor vessel traffic.
[0023] As shown in FIGS. 2-3, vessel 15 passing through the harbor
can generate a Kelvin wake. As the Kelvin wake propagates, it can
reach the buoys and act on the buoys 12. The buoys 12 can have six
degrees of motion: heave, sway, surge, roll, pitch and yaw. The
first three are translational motion degrees of freedom, heave can
be in the vertical direction, while sway and surge can be in the
horizontal directions (for example, the x-axis and y-axis
respectively, as shown in FIG. 3). Rolling is a rotation around a
longitudinal axis (x-axis), pitching is a rotation around the
transverse axis (y-axis) and yawing is a rotation around the
vertical axis (z-axis). As the buoy(s) move in one or more the
degrees of motion, that motion can be recorded within the buoys as
buoy position data.
[0024] As shown in FIG. 3, each buoy 12 can be in wireless
communication with a receiver 14 for receiving buoy position data,
which can be sent from each buoy 12. A processor 16 can receive the
aggregate buoy position data, and using non-transitory written
instructions, can provide a determination as to whether the buoy
position data indicates the presence of vessel 15. The
determination can be shown at display 18. The instructions can be
in the form of a fuzzy neural network. Fuzzy neural network
algorithms can be thought of as a learning machine that finds the
parameters of a fuzzy system (i.e., fuzzy sets, fuzzy rules) by
exploiting approximation techniques from neural networks. Fuzzy
neural network can be used for the systems and methods of the
present invention because in the real world, all physical systems
and nature behaviors are non-linear (fuzzy). Fuzzy neural networks
can be the only algorithm that can have hybrid learning capacity
using both raw fixed buoy data and generalized linguistic terms
that describe behaviors of output classes (such as speed, proximity
and heading, type of the vessel.) As such, fuzzy neural networks
can be the only available algorithm able to describe a mathematical
function from learned relationships. The algorithm can use data
from consecutive asynchronous 3D movement of the water and
correlates it with moving object descriptors.
[0025] As an example of the above, different object that create
wakes on the surface of the water have different characteristics,
for example speed of vessel 15 can create the Kelvin wake with
sharp consecutive asynchronous movement recorded by accelerometer
and gyroscope placed on the buoys. Slower speed boats will record
gentle movement trends and slower wave decay. For example, if the
speed of the boat is 12 mph, and it passes buoy array 10 feet away,
the data patterns depicted in FIG. 4 and FIG. 5 can be created.
Curves 60, 62 and 64 in FIG. 4 can depict translational
acceleration data for this scenario in the x-, y- and z-directions,
respectively. Curves 66, 68 and 70 in FIG. 5 depict yaw, roll and
pitch, respectively, over a 10 minute period. Table 1 below is a
listing of the graphed data from FIGS. 4-5 over the first 6 seconds
of the curves in FIGS. 4 and 5:
TABLE-US-00001 TABLE 1 Translational Acceleration Versus Time Buoy
1 (sec) 00:00 00:01 00:02 00:03 00:04 00:05 00:06 Acc X (m/s2) 15
10 14 -20 -15 -12 15 Acc Y (m/s2) -15 -12 -12 -20 -15 -14 13 Acc Z
(m/s2) 10 4 -12 -20 -13 15 20 Yaw (deg/s) 3 3 -3 5 6 7 -3 Roll
(deg/s) 3 -5 -7 2 -5 -1 -1 Pitch (deg/s) -2 -5 -8 1 2 0 -3 Compass
SW W W 3 ES ES W
[0026] Each buoy 12 can record the data due to its motion caused by
the Kelvin wave, in order to determine to the sensing acceleration
and direction of the Kelvin wakes (and by extension, the course and
speed of the vessel 15 that caused the Kelvin wake). Depending on
the purpose of use, pattern of placement, and area surveillance,
multitudes of these buoys 12 can be used to map natural and
man-made Kelvin Wakes.
Object's Data
[0027] Data from the array of buoys 12 can be correlated with the
experimental data which may have been previously taken and
recorded. The experimental data can include measurement of
asynchronous movement of buoy arrays, with recorded data of known
object characteristics. One such object characteristic can be
speed. Speed can be a categorical representation of objects speed
on the surface of subsurface of the water. For the simplicity of
calculations speed then categorized into 3 different speeds such as
low (0 to 25 mph), medium (25 to 35 mph) and high above (35 mph and
above). Another such characteristic can be proximity. Proximity can
be a categorical representation of the position of vessel 15 on the
surface or subsurface of the water relative to at least one of the
buoys 12. For the simplicity of calculations the proximity is then
categories into 3 different measures such as: close (up to 10
feet), medium (10-40 feet) and far (40 and above).
[0028] Another object characteristic can be heading. Heading can be
the angle between the direction in which the vessel 15 is moving
and a reference direction of the line of buoy arrays. (Typically
true north, but other cardinal directions or headings could be
used. The size of vessel 15 can be represented by the physical size
recorded in feet (6 feet) controlled by the motor, wind or human
power. Using these object characteristics, alternative result data
can be generated by the systems and methods of the present
invention. The alternative data can be illustrated in Table 2
below.
TABLE-US-00002 TABLE 2 Object Result Data Object 1 (sec) 00:00
00:01 00:02 00:03 00:04 00:05 00:06 Speed (mph) 15 10 14 20 15 12
15 Proximity (feet) 5 10 20 10 15 14 13 Heading (deg) 10 4 -12 -20
-13 15 20 Compass (deg) SW W W 3 ES ES W
Artificial Neuro-Fuzzy Algorithm
[0029] Referring now to FIGS. 6-7, all the data cited assembled
supplied to an artificial neuro-fuzzy (ANF) algorithm (as used
herein, "ANF" and fuzzy neural can be taken to mean the same
thing). The input data represents temporal displacement of buoy 12.
The output data can represent the observable object
characteristics. The data obtained from buoy 12, which has been
placed on subsurface of the water, is observable and "learned".
[0030] In an alternative embodiment, and referring now to FIG. 7,
the input data can be the data of Kelvin wakes recorded by array of
buoys 12 that can correlate to the type of the vessel producing
wakes, recording non-linear wake timing interval characteristics of
Kelvin patterns, at known speeds, proximities and headings.
[0031] The box 34 in FIGS. 6-7 of the algorithms can be
representative of the following logic. The data of at least 30 min
of input-output pairs have been supplied into Artificial Neuro
Fuzzy Algorithms for training. Each data input-output pair can have
the following relation, described by a formula:
Out.sub.i=.sigma.(AccX*w.sub.1+AccY*w.sub.2+ . . .
Pitch*w.sub.6+.theta.), where [0032] .theta.--is the bias; [0033]
w--is the weight assigned to the six degrees of freedom in the
formula; [0034] .sigma.--is a sigmoid activation function
[0034] .sigma. ( x ) = 1 1 + e - x ; ##EQU00001##
and, [0035] AccX--data point representing acceleration over X
plain.
[0036] To train, test and use the system 10 to use available data,
a fuzzy neural back-propagation algorithm can be used. Once such
algorithm can be described in a paper by Iryna Petrosyuk, entitled
"Neuro-fuzzy Model for Image Processing in Electro-optical
Applications," 2006 International Conference--Modern Problems of
Radio Engineering, Telecommunications, and Computer Science,
Lviv-Slavsko, 2006, pp. 218-221. The contents of the Petrosyuk
paper are hereby incorporated by reference herein.
[0037] Referring now to FIGS. 8A-8B, the internal components of
each buoy 12 can be seen in greater detail. Buoy 12 can include an
upper buoy portion 13a as shown in FIG. 8A, and a lower buoy
portion 13b, as shown in FIG. 8B. Each buoy 12 can further include
an accelerometer 20, a gyroscope 22, a Global Position Satellite
(GPS) component 24, a compass 26, and data storage 28. These
components can be fixed to lower portion 13b (or to upper portion
13a if more convenient). The accelerometer and compass components
can the LSM6DS33 and LIS3MDL Carrier models manufactured by Pololu
Robotics and Electronics, while the gyroscope could be a Pololu
MinIMU-9v5. Other vendors that provided similar components could be
used. The accelerometer 20, gyroscope 22, a Global Position
Satellite (GPS) component 24 and compass 26 can provide buoy
position data either directly to an antenna 30 (A Pololu 66-Channel
LS30031 by Pololu could be used for the antenna) for transmission
to receiver 14, or to data storage 28 if antenna is temporarily
inoperative, or to both data storage 28 and antenna 30
simultaneously. A power source 32 can be used to provide power to
the buoy components. The power source can be a renewable type,
which can use either solar power, or power which can be generated
from the wave motion of the buoy 12. The buoy 12 can be an
efficient, lower cost sensor that requires very little or even no
maintenance once the buoys are placed in the system.
[0038] Referring now to FIG. 9, a block diagram 50 is shown, which
can be used to describe the methods of the present invention
according to several embodiments. As shown, method 50 can include
the initial step 52 of placing buoys in a harbor in a predetermined
pattern. The pattern can be a straight line of equidistant buoys
50, or it pattern could be customized according to considerations
such as number of buoys available and harbor geography, as long as
the spacing distance d.sub.i and pattern geometry is known and is
accounted for in the fuzzy neural network algorithm used by
processor 16. Next, the methods 50 can include the step 54 of
determining buoy position data. The buoy position data can be
determined using the structure and cooperation of structure
described above for buoys 12.
[0039] The methods 50 can further include the step 56 of
transmitting buoy position data to a remote receiver 14. Then, and
as shown by step 58, the buoy data can be converted into an
indication of vessel presence in the harbor. The conversion can be
accomplished by a fuzzy network neural algorithm. Other types of
algorithms could also be used to accomplish the method according to
several embodiments.
[0040] From the above, it can be seen that Kelvin wake phenomena
can have a waveform that can be measured using traditional signal
processing techniques. The Kelvin wake travel parameters can be
measured by the buoy as buoy movements along the buoy degrees of
freedom (which are caused by the Kelvin wake) as buoy position
data. The buoy(s) position data set(s) readings can be sent by RF,
cellular signal or fiber optics to the server via receiver 14.
These signals are analyzed in real-time by a fuzzy neural network
algorithm. Once man-made patterns are detected the server sends out
notification, which can be seen at display 18 by a remote operator
(not shown).
[0041] The present invention according to several embodiments can
provide several advantages. Embodiments of this invention can use a
low power, low cost array of buoys that can detect and recognize
Kelvin wake patterns and send real-time notifications
automatically, without human intervention. In addition, because of
image and video recording of radar imagery ships wakes are seen at
better accuracy then the actual ship imagery, there is a high need
to be able to recognize ship's types from the Kelvin Wake pattern
alone. As described above, processor 16 can further utilize an
adjustable fuzzy neural network algorithm that can be aware of and
respond to changing environmental and vessel traffic
conditions.
[0042] The advantages of this method can further include the use of
a low power, renewable energy source; gives real-time notifications
on time type, size, speed and direction of moving object. Other
methods are limited in low visibility (image and video records) can
require substantial power or human operator. Still other
embodiments of the present invention can be used as a method to
monitor movement on water surface for purposes such as drug
trafficking, pool safety, renewable energy source, farm fishing,
marine species monitoring and littoral security. Such systems can
be used by a low power, low cost array of buoys that can detect and
recognize Kelvin wake patterns from naturally occurring surface and
internal waves and send real-time notifications automatically,
based on an analysis of those wave patterns. These systems can also
be deployed autonomously and be discretely placed along
shorelines.
[0043] The use of the terms "a" and "an" and "the" and similar
references in the context of describing the invention (especially
in the context of the following claims) is to be construed to cover
both the singular and the plural, unless otherwise indicated herein
or clearly contradicted by context. The terms "comprising",
"having", "including" and "containing" are to be construed as
open-ended terms (i.e., meaning "including, but not limited to,")
unless otherwise noted. Recitation of ranges of values herein are
merely intended to serve as a shorthand method of referring
individually to each separate value falling within the range,
unless otherwise indicated herein, and each separate value is
incorporated into the specification as if it were individually
recited herein. All methods described herein can be performed in
any suitable order unless otherwise indicated herein or otherwise
clearly contradicted by context. The use of any and all examples,
or exemplary language (e.g., "such as") provided herein, is
intended merely to better illuminate the invention and does not
pose a limitation on the scope of the invention unless otherwise
claimed. No language in the specification should be construed as
indicating any non-claimed element as essential to the practice of
the invention.
[0044] Preferred embodiments of this invention are described
herein, including the best mode known to the inventors for carrying
out the invention. Variations of the preferred embodiments may
become apparent to those of ordinary skill in the art upon reading
the foregoing description. The inventors expect skilled artisans to
employ such variations as appropriate, and the inventors intend for
the invention to be practiced otherwise than as specifically
described herein. Accordingly, this invention includes all
modifications and equivalents of the subject matter recited in the
claims appended hereto as permitted by applicable law. Moreover,
any combination of the above-described elements in all possible
variations thereof is encompassed by the invention unless otherwise
indicated herein or otherwise clearly contradicted by context.
* * * * *